Skill learning and task outcome prediction for manipulation

2011

Conference Paper

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Learning complex motor skills for real world tasks
is a hard problem in robotic manipulation that often requires
painstaking manual tuning and design by a human expert.
In this work, we present a Reinforcement Learning based
approach to acquiring new motor skills from demonstration.
Our approach allows the robot to learn fine manipulation skills
and significantly improve its success rate and skill level starting
from a possibly coarse demonstration. Our approach aims to
incorporate task domain knowledge, where appropriate, by
working in a space consistent with the constraints of a specific
task. In addition, we also present an approach to using sensor
feedback to learn a predictive model of the task outcome. This
allows our system to learn the proprioceptive sensor feedback
needed to monitor subsequent executions of the task online and
abort execution in the event of predicted failure. We illustrate
our approach using two example tasks executed with the PR2
dual-arm robot: a straight and accurate pool stroke and a box
flipping task using two chopsticks as tools.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems